Interest-based video streams

ABSTRACT

Embodiments related to delivering a video stream to a requesting viewer are disclosed. In one example embodiment, a video stream is delivered by receiving a request for the video stream from a requesting device, the request including the requesting viewer&#39;s identity, assembling a list of selected video content items for display to the requesting viewer, the selected video items being selected from a plurality of video items based on the identity of the requesting viewer and a viewing interest profile comprising the requesting viewer&#39;s viewing interests, and sending the video stream for display, the video stream including one or more of the selected video items.

BACKGROUND

Video content may be obtained from a continually growing number ofcontent sources via a diverse set of communications mediums. Forexample, digital cable television and/or satellite television may enablethe broadcast of hundreds of channels of content. Likewise, video may beprovided over a computer network via a potentially unlimited number ofsources. As a result, a viewer may find it increasingly challenging todiscover interesting and relevant content.

SUMMARY

Various embodiments are disclosed herein that relate to delivering avideo stream to a requesting viewer. For example, one embodimentprovides a method of providing video content comprising receiving arequest for the video stream from a requesting device, the requestincluding the requesting viewer's identity. A list of selected videocontent items is then assembled for display to the requesting viewer,the selected video items being selected from a plurality of video itemsbased on the identity of the requesting viewer and a viewing interestprofile comprising a representation of the requesting viewer's viewinginterests. A video stream comprising one or more of the selected videocontent items is then sent for display to the requesting viewer.

This Summary is provided to introduce a selection of concepts in asimplified form that are further described below in the DetailedDescription. This Summary is not intended to identify key features oressential features of the claimed subject matter, nor is it intended tobe used to limit the scope of the claimed subject matter. Furthermore,the claimed subject matter is not limited to implementations that solveany or all disadvantages noted in any part of this disclosure.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 schematically shows a viewer watching a video stream within avideo viewing environment according to an embodiment of the presentdisclosure.

FIGS. 2A-B show a flow diagram depicting a method of delivering a videostream to a requesting viewer according to an embodiment of the presentdisclosure.

FIG. 3 schematically shows a viewer emotional response profile, aviewing interest profile, and an aggregated viewer emotional responseprofile according to an embodiment of the present disclosure.

DETAILED DESCRIPTION

Broadcast television has long been a one-to-many channel, pushing outprogramming to a large number of viewers without providing a real-timefeedback loop for viewer feedback. This model made customization andprovision of video streams on a per-viewer basis difficult. As a result,the opportunities for viewers to view desired programming were limitedto the pre-scheduled programming on available channels.

More recently, video recording devices and an expansion in the channelsthough which content may be accessed has facilitated the ability ofusers to watch desired content on a desired schedule. For example, iftwo desired television shows are broadcast at the same time, a user mayrecord one while watching the other, and then view the recorded show ata later time.

Likewise, the user also may access the other show at a later time bystreaming video from a website of the content provider. The developmentof streaming video delivery via a network has greatly increased thenumber of content sources available, and also allows users to accessprogramming at any desired time, rather than being limited by broadcasttime. Additionally, digital cable, broadcast and satellite televisionhas greatly increased a number of channels available for viewing.

However, such factors have also made content discovery challenging. Forexample, as mentioned above, digital and satellite television mayprovide hundreds of available channels. Further, many online sources ofcontent have extremely large content collections. While a user maylocate digital or satellite content via an electronic programming guide,discovery of online content may be much more difficult. For example,search engines may have difficulty locating specific content on anunaffiliated video content provision website. Therefore, viewers wishingto discover such content may resort to performing searching on eachindividual video content site.

In light of such issues, the disclosed embodiments utilize the detectionof various attributes of a user, including but not limited to emotionalstate and social network connections, to discover potentiallyinteresting video content for the user. The disclosed embodimentsfurther relate to entertainment systems including viewing environmentsensors to assist in determining viewer preferences for use in helpingviewers to discover content.

In some embodiments, selection of video items also may be based on theemotional responses of other viewers to those video items, as well as onthe viewing interests of the requesting viewer. Further, videoselections may be customized based on other factors, such as a time ofday when the video stream is to be presented to the requesting viewer, ageographic location selected by the requesting viewer, the requestingviewer's social network(s), and Internet browsing interests of therequesting viewer.

Examples of viewing environment sensors that may be used to gather datafor use in sensing emotional state include, but are not limited to,image sensors, depth sensors, acoustic sensors, and potentially othersensors such as motion and biometric sensors. Such sensors may allowsystems to identify individuals, detect and understand human emotionalexpressions, and provide real-time feedback while a viewer is watchingvideo. Based on such feedback, an entertainment system may determine ameasure of a viewer's enjoyment of the video item and provide real-timeresponses to the perceived viewer emotional responses. Further,emotional responses of viewers to video items may be aggregated and fedto content creators.

FIG. 1 schematically shows an embodiment of a video viewing environment100 in which a viewer 101 is viewing a video item 102 on a displaydevice 103. Display of the video items may be controlled by computingdevices, such as media computing device 104, or may be controlled in anyother suitable manner. The media computing device 104 may comprise agame console, a set-top box, a desktop computer, laptop computer,notepad computer, or any other suitable computing device, and comprisesa display output configured to output data to display device 103 fordisplay

An embodiment of a video viewing environment sensor system 106 is shownconnected to media computing device 104 via a peripheral input at whichthe media computing device receives sensor data to media computingdevice 104.

In turn, computing device 104 may generate emotional response profilesof the viewers for the video items, and send the emotional responseprofiles via a network 110 to a server computing device 120. Theemotional response profile comprises a temporal record of the viewer'semotional response to the video item being displayed in the videoviewing environment. Put another way, the viewer's emotional responseprofile for the video item represents that viewer's emotionalexpressions and behavioral displays as a function of a time positionwithin the video item.

Server computing device 120 may be configured to, for each of the videoitems, synthesize the emotional response profiles from a plurality ofviewers into an aggregated emotional response profile for that videoitem. Later, a particular video item may be selected for inclusion in alist of selected video items to be displayed to a viewer requesting avideo stream. Selection of the particular video item may be based on theidentity of the requesting viewer and the requesting viewer's viewinginterests as included in a viewing interest profile.

The viewing interest profile for a requesting viewer and/or for a personor people in a group of potentially positively correlated viewers (e.g.that may be likely to respond to a video item in a similar manner as arequesting viewer as determined by a common interest, socialcommonality, relationship, or other link between the viewers) may thenbe used to select video items for inclusion in the video stream that maybe of potentially greater interest to the requesting viewer. Forexample, a particular video item may be selected based on an intensityor magnitude of an emotional response of the plurality of viewers whoseresponses are included in the aggregated emotional response profile tothe video item, and/or to aspects of portions of the video item (e.g.objects, scenes, etc. in the video item).

Further, a video item may be selected based on a current emotionalstatus of the requesting viewer. By tailoring video item selection tothe requesting viewer, it may be comparatively more likely that therequesting viewer will find the video stream interesting and emotionallystimulating, which may enhance the effectiveness of the entertainmentexperience and/or help the requesting viewer discover new video content.

Video viewing environment sensor system 106 may include any suitablesensors, including but not limited to one or more image sensors, depthsensors (such as a structured light, time of flight, or stereo depthcamera), and/or microphones or other acoustic sensors. Data from suchsensors may be used by media computing device 104 to detect postures andgestures of a viewer, which may be correlated by media computing device104 to human affect displays. It will be understood that the term “humanaffect displays” as used herein may represent any detectable humanresponse to content being viewed, including but not limited to humanemotional expressions and/or detectable displays of human emotionalbehaviors, such as facial, gestural, and vocal displays, whetherperformed consciously or subconsciously.

As a more specific example, image data received from viewing environmentsensor system 106 may capture conscious displays of human emotionalbehavior of a viewer, such as an image of a viewer 101 cringing orcovering his face. In response, the viewer's emotional response profilefor that video item may indicate that the viewer was scared at that timeduring the video item. The image data may also include subconsciousdisplays of human emotional states. In such a scenario, image data mayshow that a user was looking away from the display at a particular timeduring a video item. In response, the viewer's emotional responseprofile for that video item may indicate that she was bored ordistracted at that time. Eye-tracking, facial posture characterizationand other suitable techniques may also be employed to gauge a viewer'sdegree of emotional stimulation and engagement with video item 102.

In some embodiments, an image sensor may collect light within a spectralregion that is diagnostic of human physiological conditions. Forexample, infrared light may be used to approximate blood oxygen levelsand/or heart rate levels within the body. In turn, such levels may beused to estimate the person's emotional stimulation.

Further, in some embodiments, sensors that reside in other devices thanviewing environment sensor system 106 may be used to provide input tomedia computing device 104. For example, in some embodiments, anaccelerometer included in a mobile computing device (e.g., mobile phonesand laptop and tablet computers, etc.) held by a viewer 101 within videoviewing environment 100 may detect gesture-based emotional expressionsfor that viewer.

FIGS. 2A-B show a flow diagram depicting an embodiment of a method 200for delivering a video stream to a requesting viewer. It will beappreciated that method 200 may be performed by any suitable hardware,including but not limited that referenced in FIG. 1 and elsewhere withinthis disclosure.

As shown in FIG. 2A, media computing device 104 includes a data-holdingsubsystem 114 that holds instructions executable by a logic subsystem116 to perform the embodiments disclosed herein. Computing device 104also may include removable and/or non-removable computer storage media118 that stores executable instructions. Similarly, the embodiment ofserver computing device 120 is depicted as including a data-holdingsubsystem 124, a logic subsystem 126, and removable and/or non-removablecomputer storage media 128.

As mentioned above, in some embodiments, sensor data from sensors on aviewer's mobile device may be provided to the media computing device.Further, supplemental/related video content related to a video itembeing watched may be provided to the requesting viewer's mobilecomputing device 130. Thus, mobile computing device 130 may beregistered with and connected to with media computing device 104 and/orserver computing device 120 to assist with performing such functions. Asshown in FIG. 2A, mobile computing device 130 includes a data-holdingsubsystem 134, a logic subsystem 136, and removable and/or non-removablecomputer storage media 138. Aspects of such data-holding subsystems,logic subsystems, and removable computer storage media as referencedherein are described in more detail below.

Returning to FIG. 2A, at 202, method 200 includes collecting sensor dataat the video viewing environment sensor and potentially from mobilecomputing device 130. Then, at 204, method 200 comprises sending thesensor data to the media computing device, which receives the input ofsensor data. Any suitable sensor data may be collected, including butnot limited to image sensor data, depth sensor data, acoustic sensordata, biometric sensor data, etc.

At 206, method 200 includes determining an identity of a viewer in thevideo viewing environment from the input of sensor data. In someembodiments, the viewer's identity may be established from a comparisonof image data collected by the sensor data with image data stored in theviewer's personal profile. For example, a facial similarity comparisonbetween a face included in image data collected from the video viewingenvironment and an image stored in the viewer's profile may be used toestablish the identity of that viewer. A viewers' identity also may bedetermined from acoustic data (e.g. by voice recognition), or any othersuitable data. Likewise, a viewer identity may be entered manually by auser (e.g. by voice, text entry device, etc.).

At 208, method 200 includes generating an emotional response profile forthe viewer, the emotional response profile comprising a temporal recordof the viewer's emotional response to the video item being displayed inthe video viewing environment. FIG. 3 schematically shows an embodimentof a viewer emotional response profile 304. As shown in FIG. 3, vieweremotional response profile 304 may be generated by a semantic miningmodule 302 running on one or more of media computing device 104 andserver computing device 120 using sensor information received from oneor more video viewing environment sensors. Using emotional response datafrom the sensor and also video item information 303 (e.g., metadataidentifying a video item the viewer was watching when the emotionalresponse data was collected and where in the video item the emotionalresponse occurred), semantic mining module 302 generates vieweremotional response profile 304, which captures the viewer's emotionalresponse as a function the time position within the video item.

In the example shown in FIG. 3, semantic mining module 302 assignsemotional identifications to various behavioral and other expressiondata (e.g., physiological data) detected by the video viewingenvironment sensors. Semantic mining module 302 also indexes theviewer's emotional expression according to a time sequence synchronizedwith the video item, for example, by times for various events, scenes,and actions occurring within the video item. In the example shown inFIG. 3, at time index 1 of a video item, semantic mining module 302records that the viewer was bored and distracted based on physiologicaldata (e.g., heart rate data) and human affect display data (e.g., a bodylanguage score). At later time index 2, viewer emotional responseprofile 304 indicates that the viewer was happy and interested in thevideo item, while at time index 3 the viewer was scared but herattention was raptly focused on the video item.

In some embodiments, semantic mining module 302 may be configured todistinguish between the viewer's emotional response to a video item andthe viewer's general temper. For example, in some embodiments, semanticmining module 302 may ignore (or may report that the viewer isdistracted during) those human affective displays detected when theviewer's attention is not focused on the display device. As an example,if the viewer is visibly annoyed because of a loud noise originatingexternal to the video viewing environment, semantic mining module 302may be configured not to ascribe the detected annoyance with the videoitem, and may not record the annoyance at that temporal position withinthe viewer's emotional response profile for the video item. Inembodiments in which an image sensor is included as a video viewingenvironment sensor, suitable eye tracking and/or face position trackingtechniques may be employed (potentially in combination with a depth mapof the video viewing environment) to determine a degree to which theviewer's attention is focused on the display device and/or the videoitem. FIG. 3 also shows an emotional response profile 304 for a videoitem in graphical form at 306 as further illustration. The emotionalresponse profile may be displayed in such form, for example, to anadvertiser and/or content creator seeking to understand viewers'reactions to the video item.

A viewer's emotional response profile 304 for a video item may beanalyzed to determine the types of scenes/objects/occurrences thatevoked positive and negative responses in the viewer. For example, inthe example shown in FIG. 3, video item information, including scenedescriptions, are correlated with sensor data and the viewer's emotionalresponses. The results of such analysis may then be collected in aviewing interest profile 308.

Viewing interest profile 308 catalogs a viewer's likes and dislikes forvideo items, as judged from the viewer's emotional responses to pastmedia experiences. Viewing interest profiles may be generated from aplurality of emotional response profiles, wherein objects, settings andother images depicted in the video item are linked to detected emotionalstates. Put another way, the viewer's emotional response profile for aparticular video item organizes that viewer's emotional expressions andbehavioral displays as a function of a time position within that videoitem. By performing such analysis for other video items watched by theviewer, as shown at 310 of FIG. 3, and then determining similaritiesbetween portions of different video items that evoked similar emotionalresponses, potential likes and dislikes of a viewer may be determinedand then used to locate video item suggestions for future viewing. Asthe viewer watches more video items, the viewer's viewing interestprofile may be altered to reflect changing tastes and interests of theviewer as expressed in the viewer's emotional responses to recentlyviewed video items. In some embodiments, the viewer's viewing interestprofile may also include information about the viewer's personalinterests (e.g., hobbies) and/or varying degrees of demographicinformation for the viewer (e.g., age, gender, location, occupation,etc.).

Turning back to FIG. 2A, the emotional responses of a plurality ofviewers to a plurality of video items are received at 212 for furtherprocessing. These emotional responses may be received at different times(for example, in the case of video items viewed by different viewers atdifferent times) or concurrently (for example, in the case of a videoitem viewed by many viewers as a live event). Once received, theemotional responses may be analyzed in real time and/or stored for lateranalysis, as described below.

At 214, method 200 includes aggregating a plurality of emotionalresponse profiles for the video item to form an aggregated emotionalresponse profiles for those video items. For example, FIG. 3 shows anembodiment of an aggregated emotional response profile 314 for a videoitem. As shown in 312 of FIG. 3, a plurality of emotional responseprofiles for a video item, each profile originating from a differentviewer, may be averaged or otherwise combined to generate aggregatedemotional response profile 314. Additionally, in some embodiments,aggregated emotional response profile 314 may also be associated withvideo item information in any suitable way (e.g., by director, actor,and location; by genre, theme, style, and length; etc.) to identifycharacteristics about the video item that triggered, to varying degreesand enjoyment levels, emotional experiences for the plurality ofviewers.

Further, in some embodiments, aggregated emotional response profile 314may be presented graphically (e.g., as a histogram or as a heatmap)depicting the relative degree and/or type of emotional stimulation as afunction of time position within the video item. Such graphicaldepictions may help video content creators identify emotionallystimulating and/or interesting portions of a video item for a group ofviewers at any suitable level of granularity (e.g., by filtering theemotional responses by social, demographic, and other suitablecriteria). In this manner, emotional responses for a broad group ofviewers to a video item may be sorted and filtered to identifyemotionally significant aspects of the video item for narrowersub-groups of viewers. Such a graphical depiction, potentially filteredbased upon a sub-group of viewers such as a social network, also may bepresented to a viewer to help the viewer discover interesting portionsof a video content item.

Continuing with FIG. 2A, at 218, method 200 includes receiving a requestfor a video stream from a requesting device, the request including therequesting viewer's identity. For example, the request may be made whenthe requesting viewer's mobile or media computing device is turned on orby input from the requesting viewer to a mobile, media, or othercomputing device. The requesting viewer's identity may be received inany suitable way (for example, the identity may be received as a user IDfor the requesting viewer). It will also be appreciated that the requestmay include image and/or sound data that is able to be matched to arequesting viewer's identity stored in a database, so that, uponmatching the image and/or sound data, the identity may be established.

Identifying the requesting viewer may assist with video item selection.For example, the viewing interest profile for the requesting viewer maybe associated with the requesting viewer's identity, so that the variousinterests and preferences of the requesting viewer may be accessed uponidentification of the requesting viewer. In some embodiments, theviewing interest profile may be obtained, based on the requestingviewer's identity, from a user account for the requesting viewer uponreceipt of the request for the video stream. User accounts may be storedon any suitable computing device (e.g., a server computing device) or ina cloud computing environment so that a requesting viewer may access apersonal user account from any number of locations.

It will be appreciated that the requesting viewer's identity may bedetermined in any suitable way, including but not limited to the vieweridentity determination schemes mentioned above. Thus, in embodimentswhere a viewer is identified via image data, a viewer may simply enterand sit down (or take another position) in a viewing environment withinthe field of view of the image sensor, be recognized, and start watchinga personalized video stream. Similarly simple scenarios may be enjoyedwith other viewer identification methods.

In some embodiments, the request for the video stream may include asearch term and/or a filter condition provided by the requesting viewer,so that selection of the first portion of the video content may be basedin part on the search term and/or filter condition. However, it will beappreciated that a requesting viewer may supply such search terms and/orfilter conditions at any suitable point within the process withoutdeparting from the scope of the present disclosure.

At 220, method 200 includes receiving information about the requestingviewer. For example, one or more of the requesting viewer's viewinginterest profile, social network information, location information,current emotional status, and a requesting viewer-provided filter and/orsearch term may be received at 220. As explained in more detail below,such information about the requesting viewer may be used to filter theaggregated emotional response profiles and/or select video items forinclusion in the video stream and potentially enhance thepersonalization of the video stream.

Filtering the aggregated emotional response profiles may identify asubset of those profiles subsequently used when selecting video itemsfor display to the requesting viewer. As a consequence, the more relatedthe group of other viewers is with the requesting viewer, the moreinteresting and relevant the video items may be to the requestingviewer.

In some embodiments, the aggregated emotional response profiles may befiltered with respect to people in a group of potentially positivelycorrelated viewers, such as members of the requesting viewer's socialnetwork. Video items that are deemed interesting to and/or arerecommended by members of the requesting viewer's social network mayalso be more likely to be interesting and relevant to the requestingviewer. Filtering the aggregated emotional responses to various videoitems by a group of people that are associated socially with therequesting viewer therefore may help to identify video items therequesting viewer might enjoy seeing. As such, method 200 includes, at222, filtering the aggregated emotional response profiles based on asocial network of the requesting viewer.

It will be appreciated that a social network may be any collection ofpeople with a social link to the requesting viewer such that therequesting viewer's interests may be potentially positively correlatedwith the collective interest of the network members. Such a network maybe user-defined or otherwise defined by a common characteristic betweenusers (e.g., alumni relationships). Additionally or alternatively, itwill be appreciated that other suitable filters may be employed withoutdeparting from the scope of the present disclosure. For example, theaggregated emotional response profiles may be filtered based ondemographic characteristics that may lead to more highly correlatedinterests between demographic group members than between all viewers.

Turning to FIG. 2B, at 228, method 200 includes assembling a list ofselected video items based on the requesting viewer's identity and/orviewing interest profile. The requesting viewer's viewing interestprofile may be used to identify specific video items and aspects ofthose video items in which the requesting viewer is interested. Forexample, FIG. 3 shows that the requesting viewer prefers Actor B toActors A and C and prefers location type B over location type A. Basedon this example, video items including Actor B and/or location type Bmay be preferentially selected for inclusion in the list.

In some embodiments, video item selection decisions may be made based ona magnitude of an emotional response of the aggregated emotionalresponse profiles for a plurality of video items. If the emotionalresponse magnitude exceeds a preselected threshold, the video item maybe deemed as being recommended for the requesting viewer and selectedfor inclusion in the list. For example, if the requesting viewer'sviewing interest profile indicates that the requesting viewer likes aparticular television show, an episode of that show that resulted in anemotional response that meets a preselected condition compared to athreshold within the aggregate audience (whether filtered or unfiltered)may be included in the list. Further, in some embodiments, another videoitem may be included in the list based on an intersection of viewinginterest profiles of viewers in the aggregated audience (again, whetherfiltered or unfiltered) that liked that episode with the viewinginterest profile of the requesting viewer.

Additionally or alternatively, video item selection decisions may bemade based on aspects of such video items (e.g., themes, actors,locations, concepts, etc.) that are associated with relatively highermagnitudes of emotional responses. For example, if the requestingviewer's viewing interest profile indicates that the requesting vieweris interested in animated movies and the like, and if aggregatedemotional response profiles for a group of viewers is relatively higherfor a graphically-intense animated television series, then an episode ofthat television series may be selected for inclusion in the list.

In some embodiments, video items may be selected for inclusion in thelist based on the requesting viewer's interests as gathered frominformation about the requesting viewer included in the requestingviewer's Internet browsing preferences. In such embodiments, 224 mayinclude, at 226, selecting video items based on Internet browsinginformation. In one example, information about websites the requestingviewer has visited (e.g., ski resort websites) may be used to selectvideo items related to skiing (e.g., an action skiing film). In anotherexample, viewing history information obtained from on-demand videostreaming websites may be used to select video items related to videocontent the requesting viewer watched at such websites.

In some embodiments, video items may be selected for inclusion in thelist based on geographic location information, which may be any suitablelocation information provided by or related to the requesting viewer. Insuch embodiments, 224 may include at, 228, selecting video items basedon location information. In some of these embodiments, the requestingviewer may select one or more geographic locations so that, even whenthe requesting viewer is not at one of those locations, the requestingviewer may obtain video items (such as news broadcasts, weatherinformation, etc.) that are relevant to those locations. For example,the requesting viewer may select her hometown as a location for whichshe would like to obtain some video items. In this example, she may beprovided with an evening news broadcast from her hometown even when shehas travelled to another location.

In some embodiments, video items may be selected based on a time atwhich the requesting viewer requests the video stream. In suchembodiments, 224 may include, at 230, selecting video items based on thetime of day. By selecting video items for inclusion based on the time ofday, live video items and/or prerecorded video items broadcast accordingto a preset schedule (e.g., a local, regional, or national broadcastschedule) may be included in the video stream even if other video itemsin the video stream are prerecorded (and are not broadcast according toa preset schedule). For example, a video stream may be programmed firstbased on those video items that have preset broadcast times and thengaps in the stream may be filled with prerecorded, unscheduled videoitems. In this way, a requesting viewer may watch a live event he isinterested in viewing, a scheduled broadcast show he watches every week,and reruns of episodes of a favorite show from his childhood,potentially without ever having to select a different video stream.

In some embodiments, the requesting viewer's current emotional state maybe used when selecting video items for inclusion in the video stream. Insuch embodiments, 224 may include, at 232, selecting video items basedon the current emotional status of the requesting viewer. By seekingpositive correlations between the requesting viewer's current emotionalstatus with and aggregated emotional response profiles for various videoitems, video items may be selected that complement the requestingviewer's current mood, potentially leading the requesting viewer toengage with and respond to the selected video items. In suchembodiments, it will be appreciated that the requesting viewer'semotional status may be obtained in any suitable way, including (but notlimited to) the semantic mining techniques described herein.

In some embodiments where selection decisions are based on therequesting viewer's emotional status, the selection of one or more videoitems may be based on the existence and/or magnitude of a positivecorrelation between the emotional status of the requesting viewer and anemotional response within the aggregated emotional response profiles forthe plurality of video items. For example, if the requesting viewer hasa lively emotional state, a video item having an aggregated emotionalresponse profile including a predetermined number or duration of scenesfor which the aggregated audience's emotional response exceeding apreselected magnitude of liveliness may be selected for inclusion in thelist.

In some embodiments, the video items selected for inclusion in the listmay be filtered using a filter applied by the requesting viewer.Further, in some embodiments, some video items may be added to the listresponsive to search terms provided by the requesting viewer. Thus, itwill be appreciated that the list may be modified in any suitable wayaccording to the preferences of the requesting viewer.

Once assembled, the list may be formed into one or more video streamsprovided to the requesting viewer. Because the various video items mayinclude live event, broadcast, and/or prerecorded video items, it willbe appreciated that a plurality of video streams may be providedconcurrently, where some of the video items are scheduled for displayaccording to a live event schedule and/or a broadcast schedule. In suchembodiments, a programming guide for the various video streams may beprovided to the viewer to help the requesting viewer decide what towatch as it is displayed and/or what to select for recording (e.g., to aDVR device) for playback at a later time. In some embodiments,therefore, method 200 may include, at 234, generating a programmingguide for one or more of the video streams.

At 236, method 200 includes sending the video stream for display, thevideo stream including one or more of the selected video items. Inembodiments where a programming guide is generated, sending the videostream may include sending the programming guide. Further, because somerequesting viewers may watch a video stream on a primary display whilechoosing to supplemental content on a mobile computing device, in someembodiments, 236 may include sending supplementary content related tothe video stream to a mobile computing device belonging to therequesting viewer. Suitable supplemental content may include (but is notlimited to) websites related to the video item being displayed on theprimary display, related advertisements, related games and/or fanparticipation opportunities, and related chat and message interfaces.

At 238, method 200 includes outputting the video stream for display at asuitable display device, such as a display device connected with one ormore of a media computing device and/or a mobile computing device. Inembodiments where supplemental content is sent, 238 may includeoutputting the supplemental content for display. Likewise, inembodiments where a programming guide is generated, 238 may includeoutputting the programming guide for display. For example, FIG. 1 showsa programming guide 170 displayed on mobile computing device 130.

In some embodiments, the methods and processes described in thisdisclosure may be tied to a computing system including one or morecomputers. In particular, the methods and processes described herein maybe implemented as a computer application, computer service, computerAPI, computer library, and/or other computer program product.

FIG. 2A schematically shows, in simplified form, a non-limitingcomputing system that may perform one or more of the above describedmethods and processes. It is to be understood that virtually anycomputer architecture may be used without departing from the scope ofthis disclosure. In different embodiments, the computing system may takethe form of a mainframe computer, server computer, desktop computer,laptop computer, tablet computer, home entertainment computer, networkcomputing device, mobile computing device, mobile communication device,gaming device, etc.

The computing system includes a logic subsystem (for example, logicsubsystem 116 of mobile computing device 104 of FIG. 2A, logic subsystem136 of mobile computing device 130 of FIG. 2A, and logic subsystem 126of server computing device 120 of FIG. 2A) and a data-holding subsystem(for example, data-holding subsystem 114 of mobile computing device 104of FIG. 2A, data-holding subsystem 134 of mobile computing device 130 ofFIG. 2A, and data-holding subsystem 124 of server computing device 120of FIG. 2A). The computing system may optionally include a displaysubsystem, communication subsystem, and/or other components not shown inFIG. 2A. The computing system may also optionally include user inputdevices such as keyboards, mice, game controllers, cameras, microphones,and/or touch screens, for example.

The logic subsystem may include one or more physical devices configuredto execute one or more instructions. For example, the logic subsystemmay be configured to execute one or more instructions that are part ofone or more applications, services, programs, routines, libraries,objects, components, data structures, or other logical constructs. Suchinstructions may be implemented to perform a task, implement a datatype, transform the state of one or more devices, or otherwise arrive ata desired result.

The logic subsystem may include one or more processors that areconfigured to execute software instructions. Additionally oralternatively, the logic subsystem may include one or more hardware orfirmware logic machines configured to execute hardware or firmwareinstructions. Processors of the logic subsystem may be single core ormulticore, and the programs executed thereon may be configured forparallel or distributed processing. The logic subsystem may optionallyinclude individual components that are distributed throughout two ormore devices, which may be remotely located and/or configured forcoordinated processing. One or more aspects of the logic subsystem maybe virtualized and executed by remotely accessible networked computingdevices configured in a cloud computing configuration.

The data-holding subsystem may include one or more physical,non-transitory, devices configured to hold data and/or instructionsexecutable by the logic subsystem to implement the herein describedmethods and processes. When such methods and processes are implemented,the state of the data-holding subsystem may be transformed (e.g., tohold different data).

The data-holding subsystem may include removable media and/or built-indevices. The data-holding subsystem may include optical memory devices(e.g., CD, DVD, HD-DVD, Blu-Ray Disc, etc.), semiconductor memorydevices (e.g., RAM, EPROM, EEPROM, etc.) and/or magnetic memory devices(e.g., hard disk drive, floppy disk drive, tape drive, MRAM, etc.),among others. The data-holding subsystem may include devices with one ormore of the following characteristics: volatile, nonvolatile, dynamic,static, read/write, read-only, random access, sequential access,location addressable, file addressable, and content addressable. In someembodiments, the logic subsystem and the data-holding subsystem may beintegrated into one or more common devices, such as an applicationspecific integrated circuit or a system on a chip.

FIG. 2A also shows an aspect of the data-holding subsystem in the formof removable computer storage media (for example, removable computerstorage media 118 of mobile computing device 104 of FIG. 2A, removablecomputer storage media 138 of mobile computing device 130 of FIG. 2A,and removable computer storage media 128 of server computing device 120of FIG. 2A), which may be used to store and/or transfer data and/orinstructions executable to implement the herein described methods andprocesses. Removable computer storage media may take the form of CDs,DVDs, HD-DVDs, Blu-Ray Discs, EEPROMs, and/or floppy disks, amongothers.

It is to be appreciated that the data-holding subsystem includes one ormore physical, non-transitory devices. In contrast, in some embodimentsaspects of the instructions described herein may be propagated in atransitory fashion by a pure signal (e.g., an electromagnetic signal, anoptical signal, etc.) that is not held by a physical device for at leasta finite duration. Furthermore, data and/or other forms of informationpertaining to the present disclosure may be propagated by a pure signal.

The terms “module,” “program,” and “engine” may be used to describe anaspect of the computing system that is implemented to perform one ormore particular functions. In some cases, such a module, program, orengine may be instantiated via the logic subsystem executinginstructions held by the data-holding subsystem. It is to be understoodthat different modules, programs, and/or engines may be instantiatedfrom the same application, service, code block, object, library,routine, API, function, etc Likewise, the same module, program, and/orengine may be instantiated by different applications, services, codeblocks, objects, routines, APIs, functions, etc. The terms “module,”“program,” and “engine” are meant to encompass individual or groups ofexecutable files, data files, libraries, drivers, scripts, databaserecords, etc.

It is to be appreciated that a “service”, as used herein, may be anapplication program executable across multiple user sessions andavailable to one or more system components, programs, and/or otherservices. In some implementations, a service may run on a serverresponsive to a request from a client.

When included, a display subsystem may be used to present a visualrepresentation of data held by the data-holding subsystem. As the hereindescribed methods and processes change the data held by the data-holdingsubsystem, and thus transform the state of the data-holding subsystem,the state of display subsystem may likewise be transformed to visuallyrepresent changes in the underlying data. The display subsystem mayinclude one or more display devices utilizing virtually any type oftechnology. Such display devices may be combined with the logicsubsystem and/or the data-holding subsystem in a shared enclosure, orsuch display devices may be peripheral display devices.

It is to be understood that the configurations and/or approachesdescribed herein are exemplary in nature, and that these specificembodiments or examples are not to be considered in a limiting sense,because numerous variations are possible. The specific routines ormethods described herein may represent one or more of any number ofprocessing strategies. As such, various acts illustrated may beperformed in the sequence illustrated, in other sequences, in parallel,or in some cases omitted. Likewise, the order of the above-describedprocesses may be changed.

The subject matter of the present disclosure includes all novel andnonobvious combinations and subcombinations of the various processes,systems and configurations, and other features, functions, acts, and/orproperties disclosed herein, as well as any and all equivalents thereof.

The invention claimed is:
 1. On a computing device, a method ofdelivering video content to a requesting viewer, the method comprising:for each of a plurality of video content items, aggregating a pluralityof emotional response profiles, each emotional response profilecomprising a temporal record of a prior viewer's emotional response to aparticular video content item when viewed by the prior viewer, to formaggregated emotional response profiles for each of the plurality ofvideo content items; receiving a request for a video stream from arequesting device, the request including the requesting viewer'sidentity; assembling a list of selected video content items for displayto the requesting viewer, the selected video items being selected fromthe plurality of video content items based on the identity of therequesting viewer and a comparison of at least a portion of theaggregated emotional response profile for each selected video item witha threshold, wherein assembling the list of selected video content itemscomprises selecting an episode of a show having an aggregated emotionalresponse magnitude that exceeds the threshold and not selecting anepisode of the show having an aggregated emotional response magnitudethat does not exceed the threshold; and sending the video streamincluding one or more of the selected video content items for display.2. The method of claim 1, wherein the selected video content items arefurther selected based on a viewing interest profile comprising therequesting viewer's viewing interests, and further comprising obtainingthe viewing interest profile for the requesting viewer from a useraccount for the requesting viewer based on the requesting viewer'sidentity.
 3. The method of claim 2, wherein the requesting viewer'sidentity is obtained based upon an image of the requesting viewercollected by an image sensor in a video viewing environment where thevideo stream is sent for display.
 4. The method of claim 1, whereinassembling the list of selected video content items comprises selectingthe selected video content items based on an emotional status of therequesting viewer received with the request for the video stream.
 5. Themethod of claim 4, wherein assembling the list of selected video itemsincludes selecting one or more of the selected video content items basedon a positive correlation between the emotional status of the requestingviewer and an emotional response within the aggregated emotionalresponse profiles for the plurality of video content items.
 6. Themethod of claim 5, further comprising filtering the aggregated emotionalresponse profiles based on a social network to which the requestingviewer belongs.
 7. The method of claim 1, wherein assembling the list ofselected video content items includes selecting the selected videocontent items based on a time of day.
 8. The method of claim 1, furthercomprising sending supplementary content related to the video stream toa mobile computing device belonging to the requesting viewer.
 9. A mediapresentation system, comprising: a peripheral input configured toreceive image data from a depth camera; a display output configured tooutput video content to a display device; a logic subsystem; and adata-holding subsystem holding instructions executable by the logicsubsystem to: collect image data capturing a requesting viewer; basedupon the image data, obtain an identity of the requesting viewer; send arequest for a video stream assembled from selected video items based onthe identity of the requesting viewer; receive the video stream, thevideo stream comprising one or more video content items each having anaggregated emotional response profile with at least a portion having amagnitude that exceeds a preselected threshold; output the video streamto the display output; receive a request to present a graphicalrepresentation of a specified aggregated emotional response profile thatillustrates emotional response data as a function of time position; andoutput the graphical representation of the specified aggregatedemotional response profile to the display output.
 10. The system ofclaim 9, further comprising instructions to detect one or more of anemotional status of the requesting viewer and the identity of therequesting viewer using the image data, wherein the request includes theone or more of the emotional status and the identity of the requestingviewer.
 11. The system of claim 9, further comprising instructions toregister a mobile computing device belonging to the requesting viewerwith the media presentation system and to deliver supplementary contentrelated to the video stream to the mobile computing device.
 12. Acomputing system for delivering a video stream to a requesting viewer ina video viewing environment, comprising: a logic subsystem; and adata-holding subsystem holding instructions executable by the logicsubsystem to: obtain, for each video item of a plurality of video items,an aggregated emotional response profile, each aggregated emotionalresponse profile representing an aggregate of a plurality of emotionalresponse profiles, each emotional response profile comprising a temporalrecord of a prior viewer's emotional response to a particular videoitem; receive an identity of the requesting viewer, the identity beingdetermined from image data obtained from a video viewing environmentsensor in the video viewing environment; receive a current emotionalstatus of the requesting viewer, the current emotional status beingdetermined from the image data; select video items for inclusion in alist of selected video items based on the identity of the requestingviewer, a viewing interest profile comprising the requesting viewer'sviewing interests, the current emotional status of the requestingviewer, and a comparison of at least a portion of the aggregatedemotional response profile for each selected video item with athreshold, wherein assembling the list of selected video items comprisesselecting an episode of a show having an aggregated emotional responsemagnitude that exceeds the threshold and not selecting an episode of theshow having an aggregated emotional response magnitude that does notexceed the threshold; and send for display a video stream including oneor more of the selected video items.
 13. The computing system of claim12, further comprising instructions to: aggregate the plurality ofemotional response profiles for each of the plurality of video items;wherein the instructions to assemble the list of selected video itemsfurther comprise instructions to select the selected video items basedon a positive correlation between the current emotional status of therequesting viewer and an emotional response within the aggregatedemotional response profiles for the plurality of video items.
 14. Thecomputing system of claim 13, further comprising instructions to filterthe aggregated emotional response profiles based on a social network towhich the requesting viewer belongs.
 15. The computing system of claim12, further comprising instructions to: receive one of locationinformation and Internet browsing information from a mobile computingdevice belonging to the requesting viewer, and wherein the instructionsto assemble the list of selected video items includes instructions toselect the selected video items based on the one of location informationand Internet browsing information.
 16. The computing system of claim 12,wherein the instructions to assemble the list of selected video itemsfurther comprise instructions to select the selected video items basedon a time of day.
 17. The computing system of claim 12, furthercomprising instructions to send supplementary content related to thevideo stream to a mobile computing device belonging to the requestingviewer.
 18. The computing system of claim 12, further comprisinginstructions to generate a programming guide for the video streampopulated with information about the selected video items and send theprogramming guide for display.